As electric vehicle (EV) manufacturing scales, innovative approaches have been evolving for battery fire protection materials to mitigate thermal runaway hazards, including fire and blast protective coatings. These materials often exhibit high-viscosity and must be deposited with uniform thickness and stable geometry to ensure reliable thermal barriers and battery pack integration. In practice, coating uniformity (uniform thickness, edge fidelity, and minimal sag/run prior to cure) is highly sensitive to dispense conditions, including nozzle slot width, nozzle length, effective dispense height (stand‑off), nozzle sweep speed, and volumetric dispense rate. Establishing robust process understanding early is therefore essential to avoid start‑up scrap, rework, and variability downstream. This paper presents a combined computational and experimental methodology to optimize and de‑risk dispensing of elastomeric battery fire protection (BFP) coatings. We develop a physics‑informed modeling framework calibrated to the coating’s rheology to predict dispense footprint and thickness uniformity, and coating pattern as functions of nozzle geometry and process parameters. The model quantifies sensitivities and trade‑offs between uniformity and throughput and is used to establish an example of set points for effective dispense height, sweep speed, dispense rate, and nozzle slot width under practical equipment constraints. An extensive experimental campaign on an OEM dispense platform systematically varies these factors to validate model predictions. Results confirm the model’s ability to rank factor influence and capture trends in uniformity, edge roll‑off, waviness, and sag/run. Working jointly with an equipment OEM and material experts (Dow), we demonstrate how model‑driven parameter selection accelerates development, tightens process windows, and improves first‑pass quality at start‑up. The validated workflow provides actionable guidance for applying elastomeric fire protection coatings reliably and efficiently in EV manufacturing.